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1,322 results for “chains”

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edi60/100

A droplet digital polymerase chain reaction assay to detect rare helminth parasites infecting natural host populations (Vancouver Island 2023, University of Wisconsin Madison Laboratory colony 2024)

Helminth infections represent a significant challenge to human, livestock, and wildlife health, yet they remain relatively under-studied, especially in terms of their ecological impacts. Better understanding of how these parasites spread in wildlife populations could improve our ability to predict and manage disease transmission across various species. Traditional detection methods, such as visually identifying parasites in environmental samples or infected hosts, often fall short, especially during the early stages of infection when parasite loads are minimal. In this study, we introduce a highly sensitive and precise droplet digital PCR (ddPCR) assay that quantifies helminth DNA in aquatic habitats, focusing on the 18S rRNA gene as a marker. These data utilize the model host-parasite system between the tapeworm Schistocephalus solidus, and its cyclopoid copepod host, Acanthocyclops robustus. The molecular assays are built around creating an infection standard in the lab, where copepods were singly infected with a single tapeworm parasite. We extracted DNA from 100 infected adults and used this as a standard to translate gene copy numbers from the ddPCR reactions to actual animal values. After creating a known lab standard, we then use the generated probes and primers to detect (and quantify!) infection burdens in field samples, which include both water filter samples (eDNA) and zooplankton tows from several lakes around Vancouver Island, B.C. The data presented here include well-specific data from ddPCR runs (amplitude of individual level oil droplets in the reaction) as well as each ddPCR analysis in its entirety. In order to prove the specificity of probes and probe-primers, we include here ddPCR runs of closely related helminth species, Schistocephalus cotti and Schistocephalus pungitii. We also consider the binding to another genera of copepod, the calanoid Eurytomora. All of the data wrangling, analysis, and data visualization are included as .Rmd files in th

openCC (other)Apr 2025View details →

zenodo52/100

Microscopic trip chains for Brunswick (Germany) region

The data set contains microscopic trip chains for the Brunswick (Braunschweig) area in Germany on an average day. All synthetic persons within Braunschweig are shown, as well as all households outside Braunschweig where at least one synthetic person had an activity in Braunschweig. The generation of this data set is based on a two-stage process. The starting point is the macroscopic transport demand model DEMO (Winkler and Mocanu, 2020: https://doi.org/10.1016/j.trd.2020.102476) and a population upscaled from the MiD 2017 ("Mobilität in Deutschland") for Germany, which was spatially distributed according to the BKG household dataset (households, inhabitants, federal government). In the first step of the process, the trip chains between the DEMO traffic cells were generated based on the daily schedules of the MiD population (Mocanu and Joshi, 2022: https://elib.dlr.de/188443/). In the second step of the process, corresponding locations were assigned within the target traffic cells. The locations were previously extracted from OpenStreeMap and attributed with activities according to their attributes/metadata (key/value pairs) (Malkus et al., 2024: https://doi.org/10.1016/j.procs.2024.06.043).

opencc-by-4.0Aug 2024View details →

zenodo48/100

Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach

Contact details: wasim.shoman at chalmers.se  waahh7 at gmail com Abstract of the research: Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO2 emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km2 square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization. The methodology: We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station. Datasets details The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure.  The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields: <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td> number of electrified trucks in 2030</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChE30</td> <td> charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td> charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td> number of electrified trucks using slow chargers (rest)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td> charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td> number of electrified trucks using fast chargers (break)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td> number of slow chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td> number of fast chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>TotCha</td> <td> Total number of chargers</td> <td>integer </td> <td>number</td> </tr> </tbody> </table>   The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format.  <table> <tbody> <tr> <td> Name </td> <td> Description </td> <td> Data Type </td> <td> Unit </td> </tr> <tr> <td> ID_origin_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_origin_region </td> <td> National name of NUTS-3 region of origin </td> <td> String </td> <td> - </td> </tr> <tr> <td> ID_destination_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_destination_ region </td> <td> National name of NUTS-3 destination region </td> <td> String </td> <td> - </td> </tr> <tr> <td> Rest </td> <td> A value of ”1” indicates a rest stop </td> <td> Boolean </td> <td> - </td> </tr> <tr> <td> Break </td> <td> A value of ”1” indicates a break stop </td> <td> Boolean </td> <td> - </td> </tr> <tr> <td> ChaDisKM </td> <td> Charged range within a trip for stopped the truck </td> <td> Float </td> <td> km </td> </tr> <tr> <td> ChaEnekWh </td> <td> Charged energy within a trip for stopped the truck </td> <td> Float </td> <td> KWh </td> </tr> <tr> <td> MainDTN </td> <td> Number of stopped trucks for the main electrification scenario (15%) </td> <td> Float </td> <td> number </td> </tr> <tr> <td> ChE30M </td> <td> Charged energy for all stopped trucks </td> <td> Float </td> <td> MWh </td> </tr> <tr> <td> ChERM </td> <td> Charged energy for the trucks stopping for rest </td> <td> Float </td> <td> MWh </td> </tr> <tr> <td> MDTN_R </td> <td> Number of trucks stopping for rest </td> <td> Float </td> <td> number </td> </tr> <tr> <td> ChEBM </td> <td> Charged energy for the trucks stopping for break </td> <td> Float </td> <td> MWh </td> </tr> <tr> <td> MDTN_B </td> <td> Number of trucks stopping for break </td> <td> Float </td> <td> number </td> </tr> <tr> <td> geometry </td> <td> X, Y coordinates </td> <td> geometry </td> <td> - </td> </tr> </tbody> </table>     The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X): <table> <tbody><tr> <th> Name </th> <th> Description </th> <th> Data Type </th> <th> Unit </th> </tr> </tbody><tbody> <tr> <td> ID_origin_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_origin_region </td> <td> National name of NUTS-3 region of origin </td> <td> String </td> <td> - </td> </tr> <tr> <td> ID_destination_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_destination_ region </td> <td> National name of NUTS-3 destination region </td> <td> String </td> <td> - </td> </tr> <tr> <td> Edge_path_E_road </td> <td> List of the network edge IDs of the shortest path between the O-D pair, determined with Dijkstra's algorithm </td> <td> String </td> <td> - </td> </tr> <tr> <td> Distance_from_origin_ region_to_E_road </td> <td> Distance from the geometric centre of the origin region to the closest network node </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Distance_within_E_ road </td> <td> Distance of the shortest edge path between the O-D pair </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Distance_from_E_ road_to_destination_ region </td> <td> Distance from the geometric centre of the destination region to the closest network node </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Total_distance </td> <td> Sum of Distance_from_origin_region_to_E_road, Distance_within_E_road and Distance_from_E_road_to_destination_region </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Traffic_flow_trucks_ 2010 </td> <td> Number of trucks that drive between the O-D pair in 2010 </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_trucks_ 2019 </td> <td> Number of trucks that drive between the O-D pair after they had been scaled to 2019 </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_trucks_ 2030 </td> <td> Number of trucks that drive between the O-D pair according to the forecast for 2030 </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_tons_ 2010 </td> <td> Number of tons that are transported between the O-D pair in 2010 according to ETISplus </td> <td> Integer </td> <td> Tons [t] </td> </tr> <tr> <td> Traffic_flow_tons_ 2019 </td> <td> Number of tons that are transported between the O-D pair after they had been scaled to 2019 </td> <td> Integer </td> <td> Tons [t] </td> </tr> <tr> <td> Traffic_flow_tons_ 2030 </td> <td> Number of tons that are transported between the O-D pair according to the forecast for 2030 </td> <td> Integer </td> <td> Tons [t] </td> </tr> </tbody> </table> Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) <table> <tbody><tr> <th> Name </th> <th> Description </th> <th> Data Type </th> <th> Unit </th> </tr> </tbody><tbody> <tr> <td> Network_Node_ID </td> <td> Unique network node ID </td> <td> Integer (6 digits) </td> <td> - </td> </tr> <tr> <td> Network_Node_X </td> <td> Longitude of the location of network node </td> <td> Float </td> <td> Degrees </td> </tr> <tr> <td> Network_Node_Y </td> <td> Latitude of the location of network node </td> <td> Float </td> <td> Degrees </td> </tr> <tr> <td> ETISplus_Zone_ID </td> <td> ID of the NUTS-3 region in which the network node is located </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> Country </td> <td> Unique country code of the country in which the network node is located (country codes are defined by ETISplus) </td> <td> String </td> <td> - </td> </tr> </tbody> </table> Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) <table> <tbody><tr> <th> Name </th> <th> Description </th> <th> Data Type </th> <th> Unit </th> </tr> </tbody><tbody> <tr> <td> Network_Edge_ID </td> <td> Unique edge ID </td> <td> Integer (7 digits) </td> <td> - </td> </tr> <tr> <td> Manually_Added </td> <td> Determines whether an edge had been manually added to the network (1) or not (0) </td> <td> Binary-integer </td> <td> - </td> </tr> <tr> <td> Distance </td> <td> Length of the network edge </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Network_Node_A_ID </td> <td> Unique ID of the network node that defines one end point of the network edge </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> Network_Node_B_ID </td> <td> Unique ID of the network node that defines one end point of the network edge </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> Traffic_flow_trucks_2019 </td> <td> Number of trucks that drive on the edge in 2019 (both highway directions combined) </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_trucks_2030 </td> <td> Number of trucks that drive on the edge in 2030 (both highway directions combined) </td> <td> Float </td> <td> Number of trucks </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2022View details →

zenodo48/100

Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR

Dataset for demographic, psychological and physiological data obtained for RESTORE (Experiment 1 WP1). Study results are published in the article titled "Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR" in the Journal of Environmental Psychology. Explanations on all variables (column names) in the datasets are given either in the second spreadsheet in each Excel file or in the csv files appended with _legend.csv (see latest version of the dataset). File 'Psychophysiological_participant_data_aggregated' is aggregated per participant (single or mean values), and the file 'Restoration_EDA_baseline-corrected_aggregated' contains EDA data aggregated per time point per restoration setting (Nature vs Urban) and prior cognitive demand condition. Methodological details on how the data was obtained and processed are given in the Open Access article.

opencc-by-sa-4.0May 2024View details →

zenodo48/100

MCMC chain of Milky Way gravitational potential models from McMillan (2017, MNRAS, 465, 76)

These are the full MCMC chains used for the main suite of results from McMillan (2017, MNRAS, 465, 76). Each line gives the parameters of a single model, with some of its derived properties, and an associated weight (the number of steps that the chain stayed at this model). The parameters are described in the README file, and further detail can be found in the original paper. The disc density profiles are of the form \(\begin{equation} \rho_d(R,z)=\left\{\begin{array}{lc}\frac{\Sigma(R)}{2z_d}\,\textrm{exp}\left(\frac{-\mid z\mid}{z_d}\right) & \textrm{for }z_d > 0 \\ \frac{\Sigma(R)}{4(-z_d)}\,\textrm{sech}^2\left(\frac{z}{2\,z_d}\right) & \textrm{for } z_d < 0,\\\end{array}\right. \end{equation}\) where \(\begin{equation} \Sigma(R)=\Sigma_0\;\textrm{exp}\left(-\frac{R_0}{R}-\frac{R}{R_d}+ \epsilon\textrm{cos}\left(\frac{\pi R}{R_d}\right)\right), \end{equation} \) with parameters \(\Sigma_0, R_d, z_d, R_0, \epsilon\) (note that \(R_0\) here is not the position of the Sun, and that \(\epsilon\) is not used). Spheroids have  \(\begin{equation} \rho_s=\frac{\rho_0}{(r^\prime/r_0)^\gamma(1+r^\prime/r_0)^{\beta-\gamma}}\; \textrm{exp}\left[-\left(r^\prime/r_{cut}\right)^2\right], \end{equation} \) where \(\begin{equation} r^\prime = \sqrt{R^2 + (z/q)^2} \end{equation} \) with parameters \( \rho_0, q, \gamma, \beta, r_0, r_{cut}\) (note that \(r_0\) is different again)

opencc-by-4.0Feb 2022View details →

zenodo48/100

Ligand binding remodels protein side chain conformational heterogeneity

While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decrease and relative hydrophobicity increases. Across a series of 13 inhibitor bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from NMR studies suggesting that residual side chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.

opencc-by-4.0Sep 2021View details →

zenodo48/100

Short Food Supply Chains Business and Marketing Models Categorisation

The present dataset contains information about the categorisation of Business Models for Short Food Supply Chains, in the context of the <a href="http://agrobridges.eu">agroBRIDGES project</a> (Horizon 2020, GA No. 101000788). Regional information about existing and new business models for Short Food Supply Chains were collected from 12 regions and countries of focus for the project (Beacon Regions) through desk research, interviews and co-creation workshops. The datasets collected information provided by Beacon Region Leaders in the co-creation workshops about models applied and newly or potentially developed ones and available in the literature, to categorise them into stylized models with specific and distinguishable features and attributes, enabling a systematic approach in the development of the sustainability assessment framework. An initial ranking of local business models was provided at a regional / country level.   Through analysis of the aggregate ranking results of business models at local level, a final list of business models for SFSCs was derived. Five different Business Model types have been addressed and developed ( i. Community Supported Agriculture - CSA, ii. Face to face trade, iii. Online Food Trade, iv. Local Food Trade, and v. Improved Logistics). The final categorization list provided valuable insights in order to represent all regional contexts and eventually offer value-added information about the main factors for developing a successful business model that can enhance market success in the long term; as well as to strengthen farmer´s strategy that eventually enhances their position within the whole value chain and improve their customer experience. The Value Proposition Canvas and Business Model Canvas was defined for each of the most common SFSC represented in each of the targeted regions. The dataset contains: • agroBRIDGES_SFSCs-BMModels-Categorisation_2021.12.01_v1 [zip file]: The initial list of business models collected from the co-creation workshops • agroBRIDGES_SFSCs-BMModels-Categorisation-Tool_2021.12.01_v1 [.xlsx file]: Internal Assessment tool for Business models regional data collection: A data collection tool was built in-house and shared to all Beacon Region Leaders in order to enable them to fill it regarding their assumptions and conclusions after the co-creation participants discussed and presented their insights from regional activities. • agroBRIDGES_SFSCs-BModels-CategorisationResults_2021.12.01_v1 [.xlsx file]: SFSCs BM type results in each of the regions. • agroBRIDGES_BusinessModels_Canvas_2021.12.01_v1 [zip file]: The Business Model and Value Proposition Canvas of the 5 BM categories

opencc-by-4.0Dec 2021View details →

zenodo48/100

MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"

These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below. The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps. The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where: <malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting). <age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr). <prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population). The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies. The files can be read into python using: <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters \(F, \gamma, \overline{\log(\rho)}\), and \(\sigma_{\log(\rho)}\), respectively.

opencc-by-4.0Sep 2022View details →

zenodo48/100

Value chains under the framework of life cycle assessment indicators

Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"

opencc-by-4.0Jan 2024View details →

zenodo48/100

Supply Chains of 36 Corporations (CERTH DB)

These are supply chains extracted from web sites and pdf files  of 36 big corporations resulting in 25,938 relationship metrics answers.

opencc-by-4.0Jan 2018View details →

zenodo48/100

Raw multibeam bathymetry data collected around the Candlemas Islands, part of the South Sandwich Islands chain in the Southern Ocean on board the R/V Akademik Tryoshnikov during the austral summer of 2016/2017 as part of the Antarctic Circumnavigation Expedition (ACE).

Dataset abstract An ELAC Nautik 3020 multibeam echo sounder with a 20 kHz transducer mounted on the hull of the R/V Akademik Tryoshnikov, was used to collect multibeam bathymetry data during the Antarctic Circumnavigation Expedition (ACE). This particular dataset was collected around the Candlemas Islands, part of the South Sandwich Islands chain in the Southern Ocean in the austral summer of 2016/2017. Bathymetry data were used live during the cruise to look for suitable locations where benthic trawling and remotely-operated vehicle deployments could take place, rather than to undertake specific bathymetric surveys. This raw dataset is provided without calibration information for the surface sound velocity or instrumentation itself and should be used with due caution. Dataset contents <ul> <li>lineYYYYDDmonHHMMSS.xse, data file, proprietary format</li> <li>lineYYYYDDmonHHMMSS.ssv, data file, ASCII</li> <li>location.hydrostar, ancillary file, ASCII</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> Dataset license This raw multibeam bathymetry dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Oct 2019View details →

zenodo48/100

SCG Dataset from Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

opencc-by-4.0Sep 2024View details →

zenodo48/100

The short gamma-ray burst population in a quasi-universal jet scenario: MCMC chains

The paper "The short gamma-ray burst population in a quasi-universal jet scenario" (https://arxiv.org/abs/2306.15488) described an effort in modelling the short gamma-ray burst population under the assumption that all jets share the same angular profile. This repository contains emcee hdf5 files with the MCMC chains corresponding to the "full sample" and "flux-limited sample" analyses described in the paper.

opencc-by-4.0Jul 2023View details →

edi48/100

Food-chain length in desert streams of central and southern Arizona, USA

## overview Food chain length (FCL) is a key measure of the vertical structure of food webs that determines energy flow through ecosystems, carbon exchange between freshwater ecosystems and the atmosphere, and rates of nutrient cycling. FCL also has a strong bearing on the biomass of green plants in ecosystems and hence on water quality in aquatic ecosystems. Broad-scale syntheses of controls on FCL in stream ecosystems indicate that FCL declines with discharge variation but, counter to theory, does not vary significantly with energy supply. The mechanisms linking discharge and energy to FCL are largely unresolved in streams. We propose that lack of a relationship between energy supply and FCL may be due to variation in efficiency of energy transfer caused by constraints of food quality, or to a temporal mismatch between measures of energy inputs and FCL. Alternatively, the effects of flow variation on FCL may simply be paramount to energy supply, but potential mechanisms linking flow to FCL remain untested. Regime shifts—punctuated change between strings of high- and low-flow events—may cause comprehensive faunal replacement across trophic levels and collapse of the vertical structure of food webs. FCL may change as a result of loss (or gain) of an apex predator, or as a result of changes in feeding relationships leading to apex predators that eat higher on the food chain. Finally, flow variation may indirectly influence FCL through inputs of limiting nutrients during floods. In desert streams, algae typically provide the primary source of energy, and algal production is limited by nitrogen (N). N loading from terrestrial ecosystems is strongly related to flow variation, particularly to the inter-flood interval (IFI) or duration of baseflow between floods. Long IFI leads to larger N pulses and potentially greater net ecosystem production (NEP), thereby providing an indirect effect of flow variation on FCL. Specific aims of the research include: 1) Quantify the effe

openCC0Jun 2022View details →

edi48/100

UCSB SONGS Mitigation Monitoring: Wetland Performance Standard - Bird Food Chain Support

These data describe annual estimates of the density of feeding birds at four coastal wetlands as part of the SONGS San Dieguito Wetland Restoration monitoring program to track long-term patterns in food chain support provided to birds. This study began in 2012 in the San Dieguito Wetlands and Tijuana Estuary in San Diego County, CA, Carpinteria Salt Marsh in Santa Barbara County, CA, and Point Mugu Lagoon in Ventura County. Beginning in 2024, Tijuana Estuary was replaced with Los Penasquitos Lagoon in San Diego County, CA.

openCC (other)Jun 2025View details →

edi48/100

Data to explore circular manureshed management in beef supply chains of the United States and western Canada

Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.

openCC (other)Jan 2023View details →

edi48/100

Thermistor chain measurements from West Lake Bonney, McMurdo Dry Valleys, Antarctica (2014-2019)

To capture year-round temperature variability in West Lake Bonney (WLB), Taylor Valley, Antarctica, we deployed two self-logging RBR Concerto T10 thermistor strings as part of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project. The thermistor strings were deployed beneath the permanent ice cover from December 2014 to November 2015 at two locations on the western side of the lake. In November 2015, both strings were relocated toward the centre of the glacier terminus to better capture potential subglacial inflows. Each thermistor string contained 10 sensors spaced at 1.44 m intervals. This data package includes 1-minute interval temperature measurements from all four deployment sites, along with daily averages compiled in a single file. Data from three thermistors were flagged as questionable based on comparisons with long-term MCM LTER CTD casts; these values remain in the dataset but are flagged to allow for adjustment during data processing.

openCC (other)Oct 2025View details →

edi48/100

Cascade Project at North Temperate Lakes LTER Temperature Chain Data 2009 - 2019

Summer temperature chain data were collected from three lakes (Peter, Paul, and Tuesday) using NexSens temperature thermistors. Data are available for five years: 2013–2015 and 2018–2019. Peter and Paul Lakes have complete records for all five years, while Tuesday Lake has data for three years. During 2013–2015, thermistors recorded temperatures at depths of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and 5.0 meters. Beginning in 2018, thermistors were added at 4.5 and 6.0 meters.

openCC (other)Feb 2025View details →

zenodo44/100

Free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz

This dataset contains calibration data of the free-field sensitivity of four electro-acoustic measuring chains at 0° incidence angle in the frequency range 0.25 kHz to 100 kHz. Each of the four channels consisted of a ¼'' externally polarized free-field measurement microphone of the condenser type GRAS 40 BF, a ¼'' preamplifier GRAS 26AC, a power module GRAS 12AQ and an FFT analyzer Ono Sokki CF-9400. The calibration data was acquired in the laboratory of the Physikalisch-Technische Bundesanstalt (PTB).

opencc-by-4.0Jan 2020View details →

zenodo44/100

Data and R Code from "A novel approach to sustainability assessment of food supply chains using networks of ecosystem services"

Data and R code from this paper applying network analysis (iGraph) to two case studies pre and post agroecological transitions in Central America and Tanzania, Africa from the IPES-Food report. Further descriptions of this data and code can be found within the extended manuscript. R Code relies on the data from the scenarios (e.g., Nodes and Relations CSVs) and creates the output network metrics (e.g., Node Metric CSVs).

opencc-by-4.0Feb 2020View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record